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Optimization of cluster-based evolutionary undersampling for the artificial neural networks in corporate bankruptcy prediction

Title
Optimization of cluster-based evolutionary undersampling for the artificial neural networks in corporate bankruptcy prediction
Authors
Kim H.-J.Jo N.-O.Shin K.-S.
Ewha Authors
신경식김현정
SCOPUS Author ID
신경식scopus; 김현정scopus
Issue Date
2016
Journal Title
Expert Systems with Applications
ISSN
0957-4174JCR Link
Citation
vol. 59, pp. 226 - 234
Keywords
Cluster-based undersampling techniqueCorporate bankruptcy predictionGenetic algorithmsImbalance data
Publisher
Elsevier Ltd
Indexed
SCIE; SCOPUS WOS scopus
Abstract
We suggest an optimization approach of cluster-based undersampling to select appropriate instances. This approach can solve the data imbalance problem, which can lead to knowledge extraction for improving the performance of existing data mining techniques. Although data mining techniques among various big data analytics technologies have been successfully applied and proven in terms of classification performance in various domains, such as marketing, accounting and finance areas, the data imbalance problem has been regarded as one of the most important issues to be considered. We examined the effectiveness of a hybrid method using a clustering technique and genetic algorithms based on the artificial neural networks model to balance the proportion between the minority class and majority class. The objective of this paper is to constitute the best suitable training dataset for both decreasing data imbalance and improving the classification accuracy. We extracted the properly balanced dataset composed of optimal or near-optimal instances for the artificial neural networks model. The main contribution of the proposed method is that we extract explorative knowledge based on recognition of the data structure and categorize instances through the clustering technique while performing simultaneous optimization for the artificial neural networks modeling. In addition, we can easily understand why the instances are selected by the rule-format knowledge representation increasing the expressive power of the criteria of selecting instances. The proposed method is successfully applied to the bankruptcy prediction problem using financial data for which the proportion of small- and medium-sized bankruptcy firms in the manufacturing industry is extremely small compared to that of non-bankruptcy firms. © 2016 Elsevier Ltd. All rights reserved.
DOI
10.1016/j.eswa.2016.04.027
Appears in Collections:
경영대학 > 경영학전공 > Journal papers
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